「engine」の検索結果

Use when legacy prompts or older framework flows reference `conversion-engineer` and you need the modern runtime to resolve that capability cleanly.

日本語の概要は準備中です。原文の説明を表示しています。

Consolidated Galyarder Framework Engineering intelligence bundle.

日本語の概要は準備中です。原文の説明を表示しています。

Use when writing PlayCanvas Engine code touching transforms, cameras, lights, materials, meshes, bounds, or imports, to apply Engine conventions and avoid legacy APIs.

日本語の概要は準備中です。原文の説明を表示しています。

Multi-step content production pipeline that takes a topic or brief and produces a full blog post, email newsletter excerpt, and three social posts — all in your voice. Useful for building a content batch, when repurposing a core idea across formats, during content sprint sessions, and for turning expertise into publishable material. Use when the user says "create content", "content pipeline", "blog to social", "write and repurpose", "content engine", or "create a blog post and social posts".

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks about phishing campaigns, social-engineering techniques, BEC (business email compromise), pretexting, AiTM (adversary-in-the-middle) kits, or specific phishing-kit families. Self-updating knowledge cell.

日本語の概要は準備中です。原文の説明を表示しています。

Detect and remove the tells of AI-generated writing in BOTH English and Spanish, and read back the evidence honestly. Use when the user asks to "de-AI" / "humanize" / "un-slop" a draft, to examine whether text carries the tells (delve, tapestry, "it's not just X, it's Y", "here's the thing", em-dash overuse, an assistant's own closing line left in the document), to compare documents for overlap, or mentions signs-of-ai / SignsOfAI. Backed by the SignsOfAI engine — for a measured 0–100 score, sentence-rhythm burstiness, originality, citations, a writer baseline or perplexity, hand off to that engine (web app, CLI or MCP server) as described below. It cannot determine who wrote a text and must never imply that it can.

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Expert prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production prompt systems with focus on reliability, efficiency, and measurable outcomes.

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Expert fintech engineer specializing in financial systems, regulatory compliance, and secure transaction processing. Masters banking integrations, payment systems, and building scalable financial technology that meets stringent regulatory requirements.

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Expert IoT engineer specializing in connected device architectures, edge computing, and IoT platform development. Masters IoT protocols, device management, and data pipelines with focus on building scalable, secure, and reliable IoT solutions.

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Use for hands-on repository work when a quiet, risk-scaled engineering method can improve design, execution strategy, implementation, material incorporation, debugging, review, delegation, workspace lifecycle, or verification. Keep clear local changes direct, preserve the complete requested outcome, and add method only where risk or uncertainty justifies it. Do not use for general technical explanations, simple file lookup, casual discussion, or non-engineering writing.

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Generative Engine Optimization plus agent readiness. Use this skill whenever the user asks about GEO, AEO, LLMO, optimizing content for AI search engines (ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini, Copilot), getting cited by LLMs, brand mentions in AI responses, llms.txt, robots.txt for AI crawlers, Schema.org JSON-LD, FAST framework, agent readiness, MCP server discovery, Web Bot Auth, OAuth for agents, API Catalog, agentic commerce (x402, ACP, UCP), Markdown content negotiation, or auditing a site or page for AI visibility. Also trigger when the user says "AI SEO", asks how to "rank in ChatGPT", "show up in Perplexity", "appear in AI Overviews", or wants a site to "speak to AI agents". Trigger even when the request is implicit, such as "review this article so AI search picks it up" or "make our docs agent-friendly".

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"Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware with per-person contributions, praise, and growth areas. Use when asked for weekly retro, what shipped this week, or engineering retrospective.".

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Package an escalation for engineering, product, or leadership with full context. Use when a bug needs engineering attention beyond normal support, multiple customers report the same issue, a customer is threatening to churn, or an issue has sat unresolved past its SLA.

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Voert de iteratieve desk-review-&-completion-cyclus uit op een regelrecht-corpus — plannen, harvesten/migreren/uitbreiden, multi-agent valideren, bevindingen 4-weg classificeren (modellering-fout / wetgevings-fout / engine-limitatie / acceptabele untranslatable), synthese + heroverweging, documenteren (wetgevings-/stelselfouten-analyse, fixes-plan, engine-limitaties, diagrammen, corpus-status), verifiëren tegen externe bronnen (resolutie-tracker + bronnen-dossier), en een eindrapport per cyclus. Gebruik dit bij corpus-review op correctheid/coverage zonder live expert-sessie, het opstellen van een wetgevings-notitie-bron, schema-migratie, het harvesten van nieuwe wetten, of het aansturen van een autonome completion-loop. Dossier-agnostisch; de regelrecht-methode is de vaste taal. Voor live-validatie met experts: zie regelrecht-audit-products.

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Maakt regelrecht-scenario's vindbaar en hun norm-ketens leesbaar/geassert. Aan de invoerkant — vertaal een platte lijst leaf-parameters naar benoemde casus-assen + een herbruikbare persona-bibliotheek, met een reverse-lookup casus-matrix. Aan de uitvoerkant — maak de norm-keten zichtbaar via keten-checkpoints (assert elke knoop op het kritieke pad, niet alleen de endpoint) en golden-trace-snapshots + branch-coverage. Gebruik dit bij het schrijven of opschonen van scenario-/feature-files, het terugvinden of ontwerpen van een specifieke casus in een grote variabelenlijst, het reviewen van engine-traces, of het regressie-bestendig maken van cross-law-ketens zodat een ketenfout niet stil achter een correcte endpoint verdwijnt. Dossier-agnostisch; de regelrecht-methode is de vaste taal. Haakt aan op regelrecht-stelselanalyse (engine-tests / meta-check) en regelrecht-audit-products (testcase-scenario's).

日本語の概要は準備中です。原文の説明を表示しています。

Works through the surviving mutants that cargo-mutants reports for packages/engine — classify each one as a real test gap, a mutant not worth killing, or dead code, then fix it and verify with `just mutants-diff`. Use when picking up the weekly "Mutatietesten" issue, when asked to improve engine test coverage, or when a pull request fails the Mutation Testing (diff) gate.

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Detection engineering skill for writing, testing, and tuning Sigma, YARA, YARA-L, Suricata, Snort, Zeek, KQL, SPL, EQL, Chronicle, and Elastic detection rules. Covers ATT&CK coverage mapping, telemetry gap analysis, false-positive tuning, detection-as-code pipelines, and validation with Atomic Red Team and Caldera. Use to design durable detections backed by tests.

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Malware reverse engineering and suspicious artifact analysis skill for defensive triage, static analysis, dynamic analysis planning, unpacking strategy, indicators of compromise, behavior summaries, YARA/Sigma ideas, and remediation guidance. Use for suspicious binaries, scripts, documents, logs, memory artifacts, sandbox reports, and malware family analysis in isolated environments.

日本語の概要は準備中です。原文の説明を表示しています。

LlamaIndex.TS data framework for RAG, indexing, retrieval, query engines, chat engines, and agentic workflows in TypeScript

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Perform static reverse engineering and analysis on Windows Portable Executable (PE) files to identify malicious indicators without executing the file. Use this skill during Incident Response, malware triage, or threat hunting to safely extract metadata, imported APIs, exported functions, embedded strings, and packed indicators from suspicious Windows binaries (.exe, .dll, .sys).

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Identify, exploit, and write custom payloads for classic Stack-Based Buffer Overflows in 32-bit and 64-bit applications. Use this skill when conducting exploit development, reverse engineering custom network protocols, or preparing for advanced certifications (OSCP, OSEP). Covers fuzzing, controlling EIP/RIP, identifying bad characters, generating shellcode, finding return/JMP instructions, and gaining reverse shells.

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Systematically extract hidden system prompts, core directives, and invisible context intentionally concealed within Large Language Model (LLM) applications. This skill utilizes targeted linguistic engineering and boundary manipulation to bypass prompt opacity.

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Engineer the full context a model sees — system prompts, retrieved knowledge, tool definitions, state, and history as one designed artifact. Use when moving beyond prompt tweaks to systematic context design.

日本語の概要は準備中です。原文の説明を表示しています。

Analytics engineering with dbt — models, tests, and modular SQL pipelines — use when building or maintaining dbt projects.

日本語の概要は準備中です。原文の説明を表示しています。